PROJECT SUMMARY Life expectancy (LE) is a critical factor in treatment decision making for men with genitourinary (GU) malignancies, since limited LE predicts lower likelihood of sufficient longevity to benefit from treatment, higher morbidity after treatment, and decreased effectiveness of treatment. Despite a prominent role of LE in guidelines, patients with limited LE are often overtreated for indolent cancers and undertreated for high-risk cancers. Data from non-cancer treatment settings suggest that this may be due to physician-level barriers precluding effective communication of LE. Surprisingly little is known about how LE is currently communicated in treatment consultations as well as patient and physician perspectives on how it should be ideally integrated. Furthermore, patient and community opinions on what LE cutoffs are best suited to ?non-aggressive treatment? are lacking. In this application, we propose a series of incremental studies with an overarching goal of building a patient-centered approach to integrating LE into treatment decision making for patients with prostate, kidney, and bladder cancer. (1) First, we will delineate how LE is currently communicated to patients with GU cancers through qualitative analysis of treatment consultation transcripts of patients with early-stage prostate, kidney, and muscle-invasive bladder cancer. We believe that the current communication of LE will be highly variable in terms of incidence of discussion, mode of communication, temporal positioning within the visit, emotive context, and time devoted to the topic. (2) Second, we will engage patient and specialist physician stakeholders through structured interviews to identify barriers and opportunities to improve communication information about LE in cancer treatment decision making. (3) Third, we will use online crowdsourcing of conjoint analysis (CA) as a platform to study how patients and the community value LE relative to other tradeoffs typically encountered in prostate, kidney, and bladder cancer treatment decision making. We will analyze crowdsourced conjoint analysis data to identify situations (based on age, comorbidity, and tumor risk) where LE appears to drive ?non-aggressive? treatment choices, which will allow for targeting of LE interventions. (4) Fourth, we will test in a randomized trial whether patient-specific LE estimates? communicated in a patient-centered approach as determined in the first and second aims and targeted to specific high-yield situations as identified in the third aim?along with LE-specific conjoint analysis data improve decisional conflict, quality of LE data discussed, and reduce overtreatment of GU malignancies. We anticipate that this patient-centered approach will improve shared decision making and ultimately result in better treatment choices for patients with GU cancers and limited LE.